数据驱动下的房地产批量评估研究综述与展望

Review and Outlook on Data-driven Real Estate Mass Appraisal

为全面呈现房地产批量评估研究的体系与脉络,文章采用文献归纳法对该领域研究文献进行系统梳理和总结。研究结果发现:(1)房地产批量评估模型与改进方面,新型的现代模型尤其是机器学习模型得到广泛的应用和讨论;(2)模型绩效评价方面,文献中采用的主要指标有R^(2)、 RMSE、 MAPE、COD等;(3)批量评估中的数据方面,数据规模和数据质量成为批量评估研究的重点问题。通过明确当前房地产批量评估研究的热点及进展,文章认为,未来研究将集中于多种模型的融合发展、多源数据的挖掘与使用以及批量评估模型的实践应用等领域。

With the aim to effectively present the system and context of real estate mass appraisal,the article employs a literature induction method to systematically organize and summarize the research literature in this field.The findings indicate that:(1)In terms of mass appraisal models and improvements,new modern models,especially machine learning models,are widely applied and dis-cussed;(2)In terms of model performance evaluation,the evaluation measures used in literatures include R^(2),RMSE,MAPE,COD and the like;(3)In terms of data in mass appraisal,data scale and quality have become key issues in mass appraisal.By clarifying the cur-rent hotspots and trend,the article proposes that future research will focus on the integration of various models,the mining and use of multi-source data,and the practical application of mass appraisal models.